Collection and analysis of self‐reported information on an ordered Likert scale is ubiquitous across the social sciences. Inference from such analyses is valid where the response scale employed means the same thing to all individuals. That is, if there is no differential item functioning (DIF) present in the data. A priori this is unlikely to hold across all individuals and cohorts in any sample of data. For this reason, anchoring vignettes have been proposed as a way to correct for DIF when individuals self‐assess their health (or well‐being, or satisfaction levels, or disability levels, etc.) on an ordered categorical scale. Using an example of self‐assessed pain, we illustrate the use of vignettes to adjust for DIF using the compound hierarchical ordered probit model (CHOPIT). The validity of this approach relies on the two underlying assumptions of response consistency (RC) and vignette equivalence (VE). Using a minor amendment to the specification of the standard CHOPIT model, we develop easy‐to‐implement score tests of the null hypothesis of RC and VE both separately and jointly. Monte Carlo simulations show that the tests have good size and power properties in finite samples. We illustrate the use of the tests by applying them to our empirical example. The tests should aid more robust analyses of self‐reported survey outcomes collected alongside anchoring vignettes.

中文翻译：

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带有锚定小插曲的分层有序响应模型的规范和测试

在有序的李克特量表上收集和分析自我报告的信息在整个社会科学中无处不在。在所采用的反应量表对所有个体都具有相同含义的情况下，从此类分析得出的推论是有效的。也就是说，如果数据中不存在差异项功能（DIF）。在所有数据样本中，所有个人和同类都不太可能具有先验性。由于这个原因，当个人按有序分类规模自我评估其健康状况（或幸福感，满意度或残障程度等）时，提出了锚定渐晕的一种校正DIF的方法。以自我评估的疼痛为例，我们说明了使用渐晕片来调整DIF的方法。使用复合分层有序概率模型（CHOPIT）。该方法的有效性取决于响应一致性（RC）和小插图等效性（VE）的两个基本假设。通过对标准CHOPIT模型的规范进行较小的修改，我们分别或联合开发了易于实现的RC和VE原假设的评分测试。蒙特卡洛仿真表明，这些测试在有限的样本中具有良好的尺寸和功率特性。我们通过将其应用于我们的经验示例来说明测试的使用。这些测试应有助于对与锚定小插曲一起收集的自我报告的调查结果进行更可靠的分析。